Do Agency Costs Influence Vertical Integration?

Do Agency Costs Influence Vertical Integration? Evidence from Regional Airlines

Silke Januszewski University of California, San Diego [email protected] Mara Lederman Rotman School of Management, University of Toronto [email protected]

Preliminary and Incomplete This Draft: July 11, 2005

Abstract This paper investigates whether agency costs affect the likelihood of vertical integration in the U.S. regional airline industry. Regional airlines provide service on short and medium haul routes under codeshare agreements with major carriers. Regionals operate flights under the brands of the major carriers and the major carriers market the regionals’ flights as their own. There is substantial heterogeneity in whether or not regionals are fully owned by the major for which they operate. Furthermore, several majors own some of their regional partners while also contracting with others. We develop a simple agency model that illustrates the costs and benefits of vertical integration in this industry. Because regionals’ flights are integrated into the major’s hub-and-spoke system, majors and their regional are inherently involved in a joint production problem such that majors are unable to provide independent regionals with optimal incentives for effort. On the other hand, because pressure to raise the salaries of regional airline employees may be harder to resist when a regional is owned, independent regionals may be have lower operating costs. The model therefore predicts that vertically integrated regionals will be used on city pairs on which the agency costs of an independent regional outweigh any lower operating costs. We test this model using data on majors’ use of regional airlines on city pairs between the top 300 airports in the second quarter of 2000. We observe the same major in many markets and thus can estimate the relationship between vertical integration and underlying market characteristics while controlling for a particular airline’s tendency to use one type of regional over the other. Our empirical findings support the predictions of the model. Vertically integrated regionals are more likely to be used on city pairs on which low effort by the regional is more costly to the major.

*We thank Ken Corts, Ignatius Horstmann, Darin Lee, Andrew Sweeting and participants at The Rotman School of Management/Institute for Policy Analysis Conference on Organizational Economics for helpful comments. All errors are our own.

I. Introduction A fundamental question in economics is which transactions should be performed inside a firm and which should be carried out in the market. In response, several “theories of the firm” have been developed. One of these - agency theory - presents vertical integration as a possible solution to asymmetric information problems that arise in the market.1 Specifically, when an agent’s effort is not observable by a principal whose payoff is affected by this effort, optimal incentives cannot be provided through a contract under many scenarios. For example, this is the case if the agent is risk averse, if there is joint production by the principal and the agent with both efforts being unobservable, or if the agent performs multiple tasks that compete for the his effort (Holmström, 1982, Holmström and Milgrom, 1991, 1994, Holmström and Tirole, 1991, Holmström, 1999). Vertical integration can mitigate these incentive problems if it makes the agent’s effort observable or gives the principal additional instruments through which to provide the agent with incentives. There is a growing empirical literature that investigates the relationship between agency costs and vertical integration.2 Studies have been carried out in the context of franchising by Lafontaine (1992) and Lafontaine and Slade (1997), in the context of trucking by Nickerson and Silverman (2003) and Baker and Hubbard (2003), and most recently, in the context of service procurement by cities by Levin and Tadelis (2004). These papers find support for the hypothesis that agency costs influence vertical integration decisions. Building on this literature, this paper investigates how agency costs affect vertical integration in the U.S. regional airline industry. Regional airlines provide service on short and medium haul routes under codeshare agreements with major carriers. The regionals operate flights under the brands of the major carriers and the major carriers market the regionals’ flights as their own. There is substantial heterogeneity in whether 1

The other leading “theories of the firm” are the transaction cost economics approach (Williamson, 1979, 1985, Klein, Crawford, and Alchian, 1978) and the property rights theory of the firm (Grossman and Hart, 1986, Hart and Moore, 1990, Hart, 1995). 2 An empirical literature investigating the transaction cost approach tests the relationship between asset specificity and vertical integration. See, for example, Monteverde and Teece (1982), Anderson and Schmittlein (1984), Masten (1984), Masten and Crocker (1985), Joskow (1985, 1987), and Hubbard (2001).

or not regionals are fully owned by the major for which it operates. Furthermore, several majors own some of their regional partners while also contracting with others. We derive and test the empirical predictions a simple agency model that illustrates the costs and benefits of vertical integration in this industry. Our model incorporates several important institutional features of the industry. Because regionals’ flights are integrated into the major’s hub-and-spoke system, majors and their regionals are inherently involved in a joint production problem. This joint production is most obvious in the context of connecting passengers who fly one leg of their itinerary on the regional and the other leg on the major. However, even for passengers flying entirely on the regional, there is still joint production in the sense that the flight is operated by the regional but marketed and ticketed by the major. As a result of this joint production, majors are unable to provide independent regionals with optimal incentives for effort on quality dimensions such as on-time performance and in-flight service. The magnitude of this agency cost is increasing in the marginal impact of the regional’s effort on the major’s revenues. In addition to the joint production problem, the level of effort chosen by a regional can also impose externalities elsewhere in the major’s network. For example, late arrival by an inbound regional flight may cause other flights operated by the major to depart late, reducing the quality of these flights to passengers onboard. Thus, passengers on other flights operated by the major may experience lower quality as result of the regional’s late arrival, despite the fact that these passengers did not, for any part of their trip, travel on the regional. We model this externality as increasing the marginal impact of the regional’s effort on the major’s revenue, thus exacerbating the incentive problem created by joint production. While there are agency costs associated with using an independent regional, these may be offset by operating cost advantages. Pressure to raise the salaries of regional airline employees may be harder to resist when a regional is owned by the major for which it is operating. As a result, independent regionals may have lower operating costs than vertically integrated ones. The model therefore predicts that vertically integrated regionals will be used on city pairs on which the agency costs of non-integration outweigh any lower operating costs.

We test this model using data on flights served by regional carriers on city pairs between the 300 largest U.S. airports in the spring of 2000. We exploit variation across “airline-city pairs” in characteristics that affect the marginal impact of the regional’s effort on the major’s revenues.

Our variation in organizational form results from

variation both between and within airlines in the types of regionals used. Since several of the major airlines use independent as well as vertically integrated regional carriers, we are able to estimate our model with airline fixed effects, using a subset of the data. The advantage of having within-firm variation in organizational form is that it allows us to investigate the relationship between vertical integration and underlying market characteristics while controlling for unobservable factors that affect a particular firm’s relative returns from vertical integration. We construct several types of variables to capture variation across airline-city pairs in the magnitude of agency costs. The first set of variables captures differences in the importance of the regional’s effort that result from the fact that low effort by the regional should have a greater effect on the quality of connecting itineraries. This is because the quality of connecting itineraries depends critically on whether passengers (and their bags) can easily connect between the two legs of their trip. We expect that vertical integration is more likely on city pairs that have a larger number of connecting passengers. The third type of variation that we use relates to differences across city pairs in the size of the externality that a late-arriving regional may impose on the major’s revenue on other flights. We expect that vertical integration is more likely on city pairs between airports at which the major operates a larger number of flights, because the potential externality is larger. The final source of variation that we exploit is differences in the average weather patterns at the endpoint airports of the city pair. If delays by a regional have a larger impact on quality when an airport is already prone to delays due to its weather patterns, then the agency costs will be larger - and vertical integration more likely - at airports with less favorable weather. We test these hypotheses using a simple logit model, conditioning on whether a city pair is served by a regional of any type. The results indicate that agency costs

influence the incentives for vertical integration in this industry. We find that vertically integrated regional partners are more likely to be used on city pairs which have the major’s hub at least one endpoint. We also find that they are more likely to be used on city pairs between airports at which the major operates a larger number of flights. Finally, city pairs between airports with less favorable weather conditions tend to be served by owned regionals.

These effects are robust to changes in the model

specification and we find qualitatively similar effects with and without airline fixed effects in the estimation. The remainder of this paper is organized as follows. The next section discusses the role of regional airlines in the U.S. airline industry and describes the types of organizational forms that govern transactions between majors and their regional partners. Section III presents a simple model of contracting between majors and regionals, in the presence of two-sided moral hazard and uses this model to motivate our hypotheses. In Section IV, we describe the data and variables. In Section V, we explain the empirical approach and present the results. A final section concludes. II. Organizational Forms in the Regional Airline Industry II.A. The Role of Regional Airlines In the U.S., regional airlines operate short and medium-haul scheduled airline service, often connecting smaller communities with larger cities. Almost all regional airlines operate under codeshare agreements with one or more major carriers.3 Under these agreements, the regional operates flights on behalf of the major carrier who markets and tickets these flights under its own code. The schedules of the regional and its codeshare partner are typically coordinated so that passengers can seamlessly connect from the regional to the major or vice versa, usually at a hub. In addition, check-in and baggage handling are typically coordinated so that passengers need only check-in and check their luggage once, at the start of their trip. In these ways, codeshare arrangements between regionals and majors are similar to the more well-known codeshare arrangements between domestic and international carriers. In those arrangements as well, each carrier sells tickets on the other’s flights under its own code. 3

In 2003, 99% of regional airline passengers traveled on flights that were codeshared with a major carrier

However, codeshare arrangements between regionals and majors differ from those between domestic and international carriers in at least two important ways.

First,

regionals typically do not sell tickets for their flights under their own codes. Rather, all ticketing is done under the major’s code. In contrast, in an international codeshare arrangement - for example, that between United Airlines and Lufthansa - United Airlines will sell seats on Lufthansa’s flights under the United Airlines code but Lufthansa will also sell seats on these flights under its own code. Second, the regional retains very little - if any - identity of its own. Specifically, the regional’s planes will be painted in the color schemes of the major for which it is operating, the regional’s flight attendants will wear the uniforms of the major, and the regional will use the logos, trademarks and even the name of the major (for example, Comair will operate for Delta under the name Delta Express). Again, none of these occur under a codeshare agreement such as United Airlines and Lufthansa’s. Although United Airlines may sell seats on Lufthansa’s flights under the United Airlines code, the flight will still be operated as a Lufthansa flight and will use the colors, uniforms and logos of Lufthansa.

Thus, in several ways, the

relationship between regionals and their codeshare partners are actually more similar to franchise agreements. Using these types of codeshare partnerships, regional airlines have come to play a vital role in the U.S. commercial airline industry, providing the sole means of scheduled air transportation at more than two-thirds of all North American airports served by U.S. carriers. Table 1 provides some descriptive data about the activity of regional airlines in 2000 - the year of our sample. In 2000, there were 94 regional airlines in operation, though 99% of activity was accounted for by the top 50 regionals. Regionals served a total of 729 airports in North America and completed 4.46 million departures. The length of the average passenger trip on a regional was short - about 300 miles - as would be expected, given the role of regionals of flying small planes (with limited ranges) on short routes that connect to majors’ hubs. From the perspective of the majors, the passengers that regionals transport to their hubs provide important feeder traffic for the mainline routes that the carriers themselves serve out of their hubs. The routes that majors have their regionals serve on their behalf are typically low-density routes which require only small aircraft - such as turboprops (9-

68 seats) and, more recently, regional jets (30-100 seats). Because of their higher labor costs4, it is not economical for the major to have its own pilots fly these small aircraft, and because of the low demand on these routes, it is not economical for the major to serve these routes with larger aircraft. As a result, majors use regionals with lower labor costs to serve these routes. II.B. Organizational Forms The codeshare relationship between a major carrier and its regional partner described above will generally be governed by one of two types of organizational forms. First, a regional may be fully owned by the major with which it partners. Second, a regional may be independently owned and may contract with one or more major carriers.5 Table 2 lists the major-regional partnerships that were in place in 2000 for the largest U.S. network carriers6. Regional carriers that appear in bold were fully owned by their major partner. Clearly, there is substantial heterogeneity both across and within carriers in the extent to which regional partners are owned. Some carriers own all of their regional partners, others own none and others use a mix of owned and independent regional carriers. Specifically, in 2000, American Airlines was the only major network carrier to use only wholly-owned regionals. American owned both American Eagle and Business Express, both of which operated under the American Eagle brand. In contrast, United Airlines and TWA both used only independent regionals. United Airlines, for example, contracted with four independent regionals (Air Wisconsin, Atlantic Coast Airlines, Great Lakes Aviation and SkyWest Airlines), each of which operated under the United Express brand. Continental, Delta, Northwest and US Airways are perhaps the most interesting (and the most useful for econometric identification) because they each used a mixture of owned and independent regionals. Delta, for example, had contract relationships with three independent regionals - Atlantic Coast Airlines, SkyWest Airlines and Trans States Airlines - which operated under the Delta Connection brand, but also had two wholly4

The mainline pilots typically manage to negotiate salaries that are substantially above the salaries paid by regional airlines. A likely explanation for this is that mainline pilot strikes are very costly for the airlines, giving relatively high bargaining power to the mainline pilots. 5 In some cases, a major will take a minority equity position in its regional partner. 6 These carriers are American, Continental, Delta, Northwest, TWA, United and US Airways.

owned regionals - Comair and Atlantic Southeast Airlines - also operating under the Delta Connection brand. Motivated by this variation, in the next section, we develop a model of joint production between regionals and majors and use this to illustrate the costs and benefits of vertical integration. Then, in the empirical work, we exploit both the cross- and within-carrier variation to investigate the characteristics of routes and airports that airlines choose to serve with owned - rather than independent - regionals. III.C. Labor Issues Relating to Regional Airlines i. Scope Clauses Both types of regionals, owned and independent, tend to have labor agreements which differ from those of the major carriers. Specifically, while still unionized, regional airline employees tend to earn lower salaries than mainline employees. This contributes to the lower operating costs of regional carriers, which is what makes them so attractive to the majors for use on short, low-density routes.

However, it is precisely this

attractiveness of regionals which causes pilots at the major carriers to worry about having too many mainline routes operated by regional partners. This concern has become particularly salient with the widespread introduction of the regional jet (RJ) in the mid-tolate 1990s. Because of their longer range and their greater appeal to business travelers, the types of routes that can be served with RJs are becoming more similar to the types of routes that were traditionally served by the major with mainline service. In contrast, because of their limited range and limited appeal to consumers, turboprop planes, which were used before the introduction of the RJ, were much less substitutable with mainline aircraft. In order to prevent the major from “replacing” them with lower cost regional service, mainline pilots have negotiated so-called scope clauses into their labor agreements. These clauses restrict the number of small planes (regional jets or turboprops) that may be operated for the major airline by regional partners, either placing on absolute limit on the number of small planes and/or tying the number of allowed small planes to increases in mainline jet flying. In the last 5-10 years, scope clauses have been a major source of conflict between management and pilots at all major carriers.

Management views these clauses as placing artificial limits on their ability to efficiently utilize lower cost RJs while pilots view these clauses as necessary to protect their jobs.7 ii. Differences in Labor Costs between Owned and Independent Regionals While the lower labor costs of regionals is what makes them attractive to the majors, the difference in salary of regional pilots and their mainline counterparts is an additional source of conflict. In particular with widespread adoption of RJs, regional pilots contend that they are providing essentially the same service as mainline pilots (at least those flying smaller jets at the mainline) for significantly lower compensation. This has lead to conflict between pilots and management at regional airlines as regional airline pilots seek higher compensation. Thus, the discrepancy between the salaries of mainline and regional pilots - which is what makes the regionals attractive partners for the majors places pressure on management to raise the salaries of regional pilots which they seek to resist because it undermines the benefits that regionals offer the majors. We believe that the pressure by regional pilots to raise their salaries leads to an important source of costs differences between owned and independent regionals. Specifically, we expect that when a regional is owned by the major for whom it operates, the pressure for the major to concede to the demands of the regional is greater because it is more difficult to distinguish the regional as a “separate” organization. For owned regionals, while substantial pressure still exists to compensate their employees in line with the more generous agreements of the major carrier, this pressure is likely weaker because the regional is a separate organization. In Table 3, we provide current data on the hourly pay rates of pilots with varying levels of experience at several different regionals. We compare pilots at owned and independent regionals operating for the same major using the same (or very similar) aircraft type. Though only a very small sample, the data in the table suggest that the labor costs of a vertically integrated regional are higher than an independent one.8

7

Of course, the scope clauses only limit the major’s ability to use regional pilots to fly RJs. They could use their mainline pilots to fly RJs, but would have to do so at much higher labor costs. 8 In addition to increased pressure on salaries, in phone conversations, airline industry executives and analysts has suggested that owning a regional is unattractive because there are costs to “managing two distinct labor forces”.

III. Theory III.A. Basic Model In this section we present a model of the trade-off between vertical integration and independent ownership in the regional airline industry. There are three central features of the model which are based on the institutional properties of this industry described in the previous section.

First, we assume that labor costs are lower for

independently owned regionals than for vertically integrated firms. Second, we assume that regional airlines are engaged in joint production with the major carriers for which they operate. For connecting passengers, it is clear that this joint production occurs. Many connecting passengers fly on a regional’s plane for one leg of their trip and on the major airline for the other leg of their trip. As a result, the quality of service received by these passengers depends on the efforts of both firms. However, the extent of joint production between the major and the regional airlines goes beyond this simple example. Even for itineraries flown entirely on a regional carrier, the major airline is involved in producing part of the service. In particular, the major airline does the ticketing, provides marketing services, and is involved – to varying degrees – in the scheduling of the flights. This implies that even for passengers whose trips only involve travel on the regional, the quality of the service they receive depends, in part, on the actions of the major. Finally, the model also accounts for the integration of the regional’s flights into the major’s route network. We incorporate this feature as a reduced form effect by allowing the regional’s effort on one route to affect the major’s profits on other flights departing from the same airport. For example, a delayed incoming plane operated by a regional carrier can adversely affect the quality of other flights by the major at the same airport by causing those flights to be delayed in departing. We call this effect an “externality”, and in our empirical work we will assume that the size of this externality varies with the number of flights that the major carrier operates at an airport.9 It is important to emphasize that the 9

The regional’s actions may impose other types of externalities on the major as well. Most notably, there may be reputation externalities whereby the provision low quality by the regional may have a negative effect on the brand reputation of the major. In this way, there is a parallel between our model and the literature on franchising. While theoretically, this externality can be modeled in the same way as the externality that the regional imposes on other flights by the major departing from the airport, we do not measure brand reputation and therefore do not emphasize this interpretation of the externality in the empirical section.

joint production and the externalities imposed by the regional carrier’s service quality on the major’s network inherently arise out of the network structure of the airline industry. Similar issues may arise in other network industries in which part of the production can be outsourced to independent contractors. We start with a model of two-sided moral hazard or team production (Holmström, 1982). We assume that the revenue on each route which is operated by a regional carrier – owned or independent – is a function of the regional carrier’s effort, eR, the major carrier’s effort, eM, and a random term ε, which is unobservable to both parties and has mean zero and a known variance, σ2. We assume that the revenue can be described by R = v(eR, eM) + ε. Both efforts are assumed to be unobservable to the other party and noncontractible. We assume that the costs of effort are given by the functions cR(eR) for the regional carrier and cM(eM) for the major carrier. Under non-integration, the regional carrier is compensated for its service according to a contract which is specified ex ante. The major carrier is the residual claimant. Its expected profits are equal to the expected revenue, v, minus the payment made to the regional carrier. Bhattacharyya and Lafontaine (1995) show that in this kind of model the optimal sharing rule can be represented by a linear contract. Based on their result, we assume that under non-integration the regional carrier is compensated according to W = αv + β.10 In addition to its cost of effort, we assume that the regional carrier also bears the costs of production, cNI. Under integration, we assume that the regional’s effort is fully observable to the major if the regional is owned by the major.11 In this case, both parties will exert optimal effort and there will be no agency costs. However, we assume that the costs of production under integration, cI, are greater than the costs of production under non-integration.

10

Two types of contracts between majors and regionals were used during our sample period. The first type are revenue-sharing agreements whereby the major and the regional share the revenue from passengers according to some specified pro-rate scheme. The second type is a fixed-fee contract whereby the major pays the regional a fixed amount to operate a particular route, independent of the number of passengers on the plane. Although data on specific contracts is not available, we estimate that during our sample period, more than 70% of regional routes were governed by revenue sharing contracts. 11 Our empirical predictions [should] hold even if the effort of the regional does not become fully observable under integration, as long as the regional’s effort is less costly to observe under integration.

We focus on a specific example to show the theoretical incentives for vertical integration.

Bhattacharyya and Lafontaine derive the optimal incentive contract for

general functional forms of v, cR and cM. However, the solution for the optimal incentive contract provides little intuition for the factors driving the agency costs. We therefore base our theoretical discussion on an example in which we assume a linear function for the expected revenue, v, and quadratic cost of effort functions. For this example, we can obtain a simple and intuitive closed form solution. We start by outlining the results of a model of joint production without the externality effect. After we have presented those results, we will show how the model changes when we account for the externality of the regional’s effort on other routes operated by the major. Assume that v ( eR , eM ) = beR + eM 2 c R ( eR ) = 1 eR 2 2 c M ( eM ) = 1 e M 2

.

For this case, we can derive the optimal revenue-sharing parameter as α =

b2 . 1 + b2

The effort levels under non-integration are eˆR = αb and eˆM = 1 − α , resulting in expected revenues of v ( eˆR , eˆM ) =

b4 + 1 . In contrast, the effort levels and the expected revenue b2 + 1

under integration are equal to eR = b , eM = 1 and v = b2 + 1 , respectively. *

*

The agency costs of non-integration, A, are given by the difference in expected revenues net of the costs of effort under integration and under non-integration. In this example, A is given by: A ≡ [v ( eR , eM ) − cR ( eR ) − cM ( eM )] − [v ( eˆR , eˆM ) − cR ( eˆR ) − cM ( eˆM )] = *

*

*

*

b4 + b2 . 2(b 4 + 2b 2 + 1)

We define the difference in operating costs between an integrated and a nonintegrated carrier as δ = cI − cNI . Vertical integration will be optimal iff δ < A . One can easily show that under non-integration, the revenue-sharing parameter and the regional carrier’s effort are increasing in b, the regional carrier’s marginal product of effort. The major carrier’s effort is decreasing in that parameter, and the agency costs, A,

are increasing in it. The model predicts that non-integration becomes more costly as the marginal product of the regional carrier’s effort increases. Now, we add the externality of the regional carrier’s effort on other routes operated by the major carrier to the model. We assume that this externality can be captured by an additive term which is linear in the regional carrier’s effort. The major’s expected revenue on a route then becomes ~ ~ v~ = beR + eM + deR = b eR + eM with b = b + d , where deR measures the externality of the regional carrier’s effort on the major carrier’s revenues. For any d > 0, taking into account the externality has the same effect as increasing the marginal product of the regional carrier’s effort. The implication is that the agency costs of non-integration are increasing in the size of the externality. To summarize our predictions, we expect that the use of vertically integrated regional carriers is more likely on routes on which the marginal product of the regional’s effort is greater. This results for two reasons. First, two-sided moral hazard in a joint production model implies that there is no incentive contract which can achieve first-best effort under non-integration. The agency cost associated with sub-optimal effort levels is increasing in the importance of the regional’s effort to revenues. Second, the externality that the regional’s effort imposes onto other flights operated by the major airline acts to increase the marginal product of the regional’s effort, further increasing the agency cost. III.B. Alternative Theoretical Framework [INCOMPLETE] According to industry sources, the three most important dimensions of a regional carrier’s service quality are flight safety, on-time performance and in-flight service. One might argue that, of those three, on-time performance is likely to be observable by the major carrier and therefore could be contracted on. If this is indeed the case, then there are two alternative theoretical models which allow for the regional’s on-time performance to be observable but still generate similar predictions as our model. Empirically, we are unable to distinguish these alternative hypotheses. The first alternative model is based on a multi-tasking framework (Holmström and Roberts, 1991). Assume that the regional’s on-time performance is observable, but its effort on flight safety and in-flight service are not.

In this case, high-powered

incentives on on-time performance will lead the regional carrier to allocate less-thanoptimal effort to the other tasks since effort on on-time performance and on safety and inflight service are substitutes. (Both compete for the time spent on the ground by the regional’s planes.) In a multi-tasking model, the agency costs of non-integration should be increasing in the marginal product of the regional carrier’s effort as in our model. The second alternative hypothesis is that on-time performance is observable but contracting on it is very costly because of the complicated relationship between the regional carrier’s effort on on-time performance and the major’s revenues, both from connecting passengers traveling on the regional and from other routes that are affected by the regional’s effort through the externality. These relationships are likely to be highly non-linear and hard to predict. Writing an incentive contract that is based on observed on-time performance requires that the airlines understand the relationship between the regional carrier’s effort on on-time performance and revenues, accounting for the impact of a large number of environmental factors such as weather, airport congestion, air traffic delay and even the actions of other carriers at the airport. If this is very costly – or impossible – then the carriers will use revenue-sharing contracts, leaving us with the model described above.

IV. Data

IV.A. Data and Sample The empirical analysis is based on airline schedule data compiled by the Official Airline Guide (OAG).

These data provide the complete weekly flight schedules of all

domestic airlines.12 Each observation in the data corresponds to a particular flight by an airline in a quarter (for example, American Airlines flight #596 between Chicago O’Hare and Atlanta in the first quarter of 2000). For each flight, the data provide information on the carrier, the origin and destination airports, the departure and arrival times, the days of operation, the type of aircraft used, the identity of any code-sharing airlines and, for flights that are operated by regional partners, the identity of the regional carrier. Recall that as described above, for flights operated by regional partners, tickets are sold only under the name of the major. 12

A representative week is provided for each quarter.

We supplement the OAG data with several additional data sources. First, we use data from the Regional Airline Association (RAA) to determine, for each flight in the OAG that is operated by a regional partner, whether that regional is owned by the major or independent.

The RAA provides annual information on every major domestic

airline’s regional partners in that year and whether or not that partner is wholly owned by the airline for which it is operating. Second, we use the 2000 Census of Population to obtain a measure of the population of the endpoint cities of each route. Third, data on average monthly temperatures and precipitation levels in each state are taken from the National Climatic Data Center.

Finally, we use the Department of Transportation

Databank 1A - a 10% sample of all domestic itineraries flown in a quarter - to construct measures of the number of passengers flying a carrier on a route and at an airport. Our sample period is the second quarter of 2000. We choose this quarter of the year because we expect that it contains the fewest number of seasonal flights. We consider all flights between the top 300 U.S. airports - a total of 4404 distinct routes.13 Because we are interested in analyzing airlines’ form of organization on a particular flight segment, our level of observation is the airline-city pair. We use the term “city pair” to refer to direct non-stop service between two endpoint airports, in either direction (i.e.: a flight from LAX-ORD and a flight from ORD-LAX are considered the same city pair). We do not distinguish the direction of service because airlines generally use the same organizational form in both directions. In some cases, majors will offer several flights per day on a city pair and will operate some set of those flights itself and others with a regional partner.14 For city pairs that are served by both the major carrier and a regional partner, we investigate the type of regional partner that is used for the subset of flights operated by the regional. We consider the organizational form decisions of the seven largest network carriers: American Airlines (AA), Continental Airlines (CO), Delta Air Lines (DL), Northwest Airlines (NW), Trans World Airlines (TWA), United Airlines (UA) and US

13

Airport rankings are based on total passenger boardings and are available at http://www.faa.gov/arp/planning/stats/2001/prim01.xls. We exclude routes that have either endpoint in Alaska, Hawaii, Puerto Rico, Guam or the U.S. Virgin Islands. 14 To maintain high frequencies, majors will use regionals to operate the flights at off-peak times of day when demand is expected to be lower.

Airways (US).15 We exclude codeshare relationships between majors and regionals in which the major sells tickets for seats on the regional under its own code but in which the regional does not operate under the name and brand of the major.16 We restrict our analysis to flights that operate on Mondays, thus only considering the airlines’ weekday schedules.17 These data restrictions result in sample of 2018 airline-route pairs of which 1661 have no missing values for any of our key variables. IV.B. Variables and Hypotheses This section describes the variables and hypotheses.

Variable names and

definitions appear in Table 4. Summary statistics appear in Table 5. i. Organizational Form Variables Our main dependent variable is OWNED_REGIONAL, a dummy variable which equals one if a flight is operated by an owned regional partner. ii. Agency Variables The theoretical model in Section III predicts that the agency costs from using a non-integrated regional are increasing in the marginal product of the regional’s effort. Given that the higher operating costs from using an owned rather than independent regional are assumed to be constant across routes, this implies that majors will be more likely to use a vertically integrated regional on routes on which the marginal product of the regional’s effort is greater. Thus, the objective of the empirical analysis will be to relate a major’s likelihood of using an owned regional on a route to characteristics of that route which measure the importance of the regional’s effort. We construct three sets of variables which measure three different sources of variation across routes in the importance of effort by a regional partner. The first set of 15

We focus on the largest network carriers because many of the predictions from the theory relate ownership decisions to characteristics of airlines’ hub operations. The results are robust to including smaller network carriers such as Midway Airlines and Midwest Express. 16 For example, we exclude the limited codeshare relationship between Northwest and American Eagle in which Northwest sells tickets on some American Eagle flights out of Los Angeles but these flights are operated under the American Eagle brand in the sense that the plane exterior and other marketing materials are associated with American Eagle, not Northwest. 17 99.46% of the flights which operate on Mondays also operate Tuesday through Friday.

variables captures variation in the importance of the regional’s effort that results from the fact that the marginal impact of a regional’s effort on quality and, in turn, revenues should be greater for passengers who are flying the regional as part of a connecting itinerary. This is because the quality of connecting itineraries depends critically on whether passengers (and their bags) can easily connect between the two legs of their trip. Late arrival by a regional may cause passengers to miss their connections which will severely reduce the quality of the trip to them. Even if passengers are able to make their connecting flights, they likely find the process of having to “race” to their gate to be unpleasant.

As well, tight connections may prevent passengers’ bags from being

transferred to the connecting flight, which also reduces the quality of the trip to consumers. More generally, connecting itineraries require greater coordination between the regional and the major. The main variable we use to capture the greater importance of the regional’s effort on routes with large numbers of connecting passengers is HUB. This is a dummy variable which equals one if either endpoint of the city pair is the major’s hub. We expect that majors will be more likely to use owned regionals on city pairs that have a hub on at least one endpoint. Although, as described in Section II, regionals are primarily used to provide feeder traffic to majors’ hubs, at least 20% of city pairs served by regionals in our sample do not have the major’s hub on either endpoint. It is these routes that provide the variation used to identify the HUB variable. To further capture variation across city pairs in the extent to which that city pair is traveled as part of direct and connecting itineraries, we construct variables that measure the actual number of passengers that traveled each city pair on each carrier in the quarter of our sample. Specifically, we construct DIRECT_PASS and CONNECT_PASS which respectively equal the number of passengers in the quarter who flew the city pair as part of direct and connecting itineraries, respectively. We calculate the sum of these two variables and call it TOTAL_PASS. Based on the intuition outlined in the previous paragraph, we expect that the marginal effect of the regional’s effort on quality is greater on connecting itineraries and therefore we expect majors to be more likely to use owned regionals on city pairs which have a larger number of passengers flying that city pair as part of connecting trips. We also expect that owned regionals are more likely to be used

on routes with a higher number of total passengers because the costs (in terms of lost revenue) associated with low quality by an independent regional occur on a passengerby-passenger basis while the labor cost benefits of using an independent regional occur at the flight level. However, we are cautious in interpreting the results from the passenger variables for several reasons. First, the passenger data is taken from the DOT DB1A which cannot be perfectly matched with the OAG schedule data.18 As a result, the passenger variables are missing for some airline-city pairs and are estimated for others. Second, these variables are endogenous. To the extent that owned and independent regionals provide different levels of quality, the number of passengers who fly on each airline on a city pair may depend on the type of regional that is used. Third, direct passengers may have a higher willingness-to-pay for quality and a higher value of time. If so, then the marginal impact of regional effort may actually be higher for direct passengers than for connecting passengers. Because this effect works in the opposite direction of the effect explained above, interpretation of the coefficients on the passenger variables may be difficult. The second type of variation across city pairs that we exploit results from the fact that a regional’s effort on a particular city pair imposes externalities on the major’s revenue on other city pairs. As discussed in Section III, this externality is analogous to an increase in the marginal impact of the regional’s effort on revenues and leads to an increase in the size of the agency cost. Thus, we expect that majors will be more likely to use owned regionals on city pairs on which the effort of the regional may impose greater externalities onto revenues on other city pairs. The type of externality that we consider is the effect of a regional’s flight that arrives late to an airport on the on-time arrival and departure of the major’s other flights at that airport (and, in turn, the major’s revenues from passengers on those flights). Specifically, when a flight operated by a regional arrives late, this may affect the on-time performance of other flights operated by the major or its regionals in two ways. First, outbound flights may be caused to depart late because they are waiting for connecting passengers who arrived late on the regional. The late departure of this outbound aircraft 18

This results from the fact that while the OAG data is very clear about the identity of the major (who tickets the flight) and the regional (who operates the flight), the DOT often does not distinguish the operating and the ticketing carrier.

will reduce the quality of this flight for all passengers onboard, even though none of these passengers actually traveled on the late-arriving regional. Second, inbound flights that are scheduled to arrive shortly after the regional’s flight may be forced to wait to access their gate and/or ground crew if the gate and crew are occupied with the late-arriving regional flight. This on-the-ground delay will reduce the quality of this flight to onboard passengers, again despite the fact that none of these passengers actually traveled on the late-arriving regional. To measure differences across city pairs in the size of these externalities, we calculate the total number of flights that the major or its regional partners operate from the endpoint airport of a city pair on a given day. The assumption underlying this variable is that the greater the number of flights the major has arriving or departing at an airport in a day, the larger the potential externality imposed by late arriving regional flights. We construct MAX_CAR_FLTS which measures the number of flights the major operates out of the endpoint at which it is larger and MIN_CAR_FLTS which measures the number of flights the major operates out of the endpoint at which it is smaller. We expect that majors will be more likely to used owned regionals on city pairs between endpoint airports at which the major operates a larger number of flights.19 The final source of variation across city pairs that we exploit is differences in the average weather patterns at the endpoint airports. To the extent that weather patterns affect an airport’s average propensity for delays and to the extent that delays by a regional have a larger impact on quality when an airport is already prone to delays, then the marginal impact of a regional’s effort on revenues should be larger at airports with less favorable weather. This may result for two reasons. First, accommodating the late arrival of a regional’s flight becomes more difficult when there are addition demands on the airline due to the weather (for example, during fog, Air Traffic Control may require longer time between departures and during ice, de-icing of the aircraft may be required). Second, if consumers’ disutility of delays is a convex function, then delays by the regional are more costly – in terms of their impact on the perceived quality of a flight 19

Note that, to some extent, the HUB variable will also capture these externalities if HUB is correlated with the number of flights the airline operates out of the airport. However, because there is variation in an airline’s size of operations at an airport – both at its hubs and non-hubs – we can include both variables in the model and, as well, interact them.

when airports are already prone to delays due to weather. Thus, we expect that majors will be more likely to use owned regionals on city pairs whose endpoint airports have less favorable weather patterns. To test this hypothesis, we construct three variables which measure the weather at the endpoint airport with “worse” weather. We define the endpoint airport with greater total annual precipitation as the airport with “worse” weather. We call this airport the “rainier” airport. For this airport, we construct RAINIER_FREEZING which counts the number of months per year in which the average temperature in the state in which the airport is located is below 40F. Next, we calculate the average annual precipitation in the state in which this airport is located and call this variable RAINIER_PRECIP_AVE. We interact RAINIER_FREEZING and RAINIER_PRECIP_AVE to account for the fact that cold weather mostly contributes to delays when it is combined with precipitation. Finally,

we

include

the

standard

deviation

of

the

precipitation

variable,

RAINIER_PRECIP_SD, again measured at the rainier endpoint. Since the relationship between weather conditions and delays is nonlinear, more variable weather leads to longer expected delays. To check the robustness of the weather results, we also construct several variations of these variables. First, we calculate these variables for the colder - as opposed to rainier - endpoint. We also calculate the averages of the weather variables across the two endpoint airports. Finally, we also include the values of the weather variables for both endpoints in the regression. The results (not reported) are robust to using these alternate weather measures. iii. Control Variables As control variables, we construct DISTANCE and MEAN_POP which, respectively, measure the distance of the route and the arithmetic mean of the populations at the endpoint airports. IV.C. Descriptive Analysis Table 5 contains summary statistics for the variables described above.

The

sample is limited to routes that are served by either type of regional. The summary

statistics indicate that slightly over half of the flights served by a regional are served by one that is wholly owned by its network carrier partner. Analyzing the decision whether to use a regional partner (of any type) on a route is not the focus of this paper. However, to provide some intuition for the types of routes on which majors use regionals, Table 6A compares the characteristics of routes served by majors with those served with either type of regional partner. The first thing to note from this table is that regional partners are much more likely to serve city pairs of a shorter distance, with an average of 314 miles compared to the major carriers’ 730 miles. City pairs served by the majors themselves are more likely to have a hub on at least one endpoint and they are more likely to fly between larger airports (as measured by the number of other flights at the airport). As expected, majors serve city pairs with much larger passenger numbers. Majors are also more likely to provide service between cities with larger populations. There are no large differences in the average weather patterns of city pairs served by majors and those served by regionals. Overall, the descriptive statistics suggest that a major’s choice between serving a city pair itself or with a regional partner is largely driven by route density and route distance. Table 6B compares the characteristics of routes served by owned and independent regionals, conditional on the route being served by either type of regional. Examination of the unconditional means in this table provides some preliminary evidence on the hypotheses laid out above. The means suggest that owned regionals are more likely to serve city pairs that have the major’s hub on at least one endpoint. They are also more likely to serve city pairs with endpoint airports at which their major operates a larger number of flights. Owned regionals are also more likely to serve routes with a greater number of passengers of any type, though they are less likely to serve city pairs with a greater fraction of connecting passengers. Finally, owned regionals are more likely to be used on city pairs that arrive or depart in states with greater total precipitation and with more variation in precipitation. Note that there are only small differences between the two types of regionals in the endpoint population and distance of the route. This suggests that these variables determine the choice between the use of the major’s own planes and that of a regional partner but do not influence the choice of the type of regional that is used.

V. Estimation and Results

V.A. Empirical Approach In this section, we present our empirical approach. We test our model of the effects of agency costs on vertical integration by investigating a major airline’s choice between an independent and an owned regional partner on a particular city pair, conditional on using some type of regional carrier to serve that city pair.

We estimate

these choices using a standard logit model. Our estimation exploits cross-sectional variation in the characteristics of city pairs. Our parameters are identified off of both cross- and within-carrier variation in the type of regional used. In some specifications, we use only the four carriers that used both types of regional partners in 2000 and include dummy variables for each carrier to control for that carrier’s propensity to use each type of regional. The results from the larger model are generally consistent with the results from the model estimated only on this subset of airlines. As described above, we have three types of agency variables that capture different sources of variation across city pairs in the marginal impact of the regional’s effort on revenues. We refer to these as the “connecting passengers variables”, the “externality variables” and the “weather variables”. The objective of the estimation is to test whether majors are more likely to use owned regional partners on city pairs on which the marginal value of the regional’s effort – as measures by these three sets of variables – is higher. Our key identifying assumption is that none of our explanatory variables are correlated with unobserved factors that affect the relative returns to using an owned or independent regional.

We also assume that any operating cost differences between owned and

independent regionals are not correlated with the agency cost variables. V.B. Results i. Simple logit estimation

We now describe the results of our empirical estimation. All tables present marginal effects. Table 7 presents our first set of results.20 The dependent variable is OWNED_REGIONAL, a dummy variable that equals one if the route is served by an owned regional and zero if the route is served by an independent regional. The sample includes all routes served by a regional of either type. Column (1) includes only the HUB variable which tests the hypothesis about connecting passengers.

The results

indicate that city pairs with the major’s hub at either endpoint are significantly more likely to be served by a regional that the major owns. Having its hub on either endpoint increases a major’s probability of using an owned regional by 0.22 percentage points. This finding is consistent with our hypothesis that the marginal impact of the regional’s effort on revenues is larger on city pairs that are primarily traveled as part of connecting itineraries.21 Column (2) adds the weather variables. Recall that these measure the weather at the endpoint airport with greater annual precipitation which is used to proxy for that endpoint having “worse” weather. Column (2) shows that all the weather variables have highly significant effects on the type of regional chosen. Owned regionals are more likely to be used on city pairs between airports with higher annual precipitation. They are also more likely to be used on city pairs between airports with greater variance in precipitation.

Both these effects are consistent with our hypothesis that vertically

integrated regional carriers are more likely to be used on city pairs with endpoint airports that are already prone to delays due to unfavorable weather patterns because the marginal impact of the regional’s effort on revenues will be greater at these airports.

Cold

temperatures - measured by the RAINIER_FEEZING variable - decrease the likelihood that the major uses an owned regional. However, cold weather on its own does not prevent flying; rather, it is the interaction of cold temperatures and precipitation (i.e.: snow and ice) that causes flight delays and cancellation. To capture this effect, column (3) adds an interaction between the cold weather and the precipitation variables. The coefficient on this interaction term is positive and significant suggesting that while cold 20

For dummy variables, the reported effects are for changing the value of the explanatory variable from 0 to 1. 21 This coefficient may also be picking up the externality effect as the number of other flights that the carrier operates from an airport will be larger at its hubs.

weather on its own does not increase the use of owned regionals, cold weather combined with precipitation does. In column (4), we add the externality variables, MAX_CAR_FLTS and MIN_CAR_FLTS and exclude the hub variable. Recall that the hypothesis about these variables is that the greater the number of other flights the major has from a particular airport, the greater the potential externality due to late arrival by the regional at that airport and the greater the marginal impact of the regional’s effort on revenues. We therefore expect that owned regionals will be more likely to be used on city pairs between airports at which the major operates a larger number of other flights. Column (4) indicates that is, indeed, the case. Increasing the number of flights operated by the major at either endpoint increases the likelihood that an owned regional is used, with the effect being larger at the smaller endpoint. In column (5), we add the hub variable back in. Although the number of flights that an airline has from an airport will surely be correlated with whether that airport is one of its hubs, there is still variation both across hub and non-hub airports in the size of an airline’s operations. Even controlling for whether one of the endpoints is a hub, increasing the number of flights that the major operates at either endpoint has a positive and significant effect on the probability of using an owned regional. As expected, the magnitudes of the HUB coefficient and the coefficient on MAX_CAR_FLTS are reduced when the two are included in the same regression as they highly correlated with each other. The coefficient on MIN_CAR_FLTS increases when HUB is included suggesting that controlling for whether one endpoint is a hub and controlling for the airline’s size at the larger airport, the major’s size of operations at the smaller endpoint affects whether an owned regional is used. Column (6) includes the log of the total number of passengers flying the city pair on the major. We expect that owned regionals will be more likely to be used on city pairs with more traffic if the agency cost from using an independent regional occurs on a passenger-by-passenger basis (especially if the operating cost advantage of using an independent regional is independent of the number of passengers).

To test this, we

include LOG(TOTAL PASS) in the regression. We find a positive and statistically significant effect. Increasing the number of passengers traveling on a city pair increases

the likelihood that an owned regional is used. Doubling the number of passengers flying the city pair increases the probability of using an owned regional by 0.15 percentage points. However, including LOG(TOTAL PASS) causes the hub and number of flights variables to be insignificant.

It is likely that these three sets of variables are too

correlated to be included in the same regression. Finally, in column (7), we distinguish direct and connecting passengers. Our hypothesis is that because poor service by a regional should have a larger impact on the quality of connecting itineraries than direct, increases in the number of connecting passengers on a route should have a larger impact on the likelihood of using an owned regional than increases in the number of direct passengers. We find a positive and significant effect of direct passengers on the likelihood of using an owned regional, but the estimate on connecting passengers is quite noisy and cannot be distinguished from zero. However, as mentioned above, we are cautious in inferring too much from these variables for several reasons.

First, they are clearly endogenous.

Second, direct

passengers may have a higher willingness-to-pay for quality which may offset the primary effect hypothesized above.

Third, the passenger variables are likely to be

correlated with the hub and number of flights variables. Overall, we find evidence in favor of our hypotheses that city pairs that have characteristics that imply a higher marginal revenue of the regional’s effort are more likely to be served using a vertically integrated regional carrier. In order to control for unobserved factors that may have airline-specific effects on the relative returns from one type of regional over the other, we now restrict the sample to the major airlines which use both types of regionals.

These airlines are Continental, Delta, Northwest, and US

Airways. We estimate the same specifications as in Table 7 on this reduced sample and include a dummy variable for each of these major carriers to control for possible airlinespecific propensity to use an owned rather than an independent regional carrier. These results are reported in Table 8 as marginal effects. Controlling for airline specific effects, the estimated coefficients generally have the same sign and most of them remain statistically significant. Looking at column (1), the hub effect is estimated to be substantially larger now than the one estimated in Table 7. Average precipitation, the standard deviation of precipitation and the interaction

between cold weather and precipitation are all still estimated to have positive and statistically significant effects on the likelihood of using an owned regional. As in Table (7), increasing the number of flights that the major operates from either endpoint increases the likelihood of using an owned major. Higher volumes of passengers are also associated with use of an owned regional, though the effects of direct and connecting passengers - when included separately in the regression - cannot be distinguished from zero. In Tables 9 and 10, we split the sample into hub and non-hub routes and reestimate the specifications from Table 7, with the hub variable, of course, omitted. This allows us to investigate whether the other variables affect both hub and non-hub routes in similar ways. However, because over 70% of the routes in our sample are hub routes, the sample size for the non-hub regressions is quite small. Looking first at hub routes (Table 9), the results are quite similar to those in Tables 7 and 8. The weather variables all have the same signs as before. MAX_CAR_FLTS and MIN_CAR_FLTS both have a positive effect on the likelihood of using an owned regional on hub route, though the coefficient on MIN_CAR_FLTS is only significant at the 10% level. As before, increasing the number of total passengers flying the major on the city pair increases the probability of using an owned regional; however, when direct and connecting passengers are distinguished, only direct passengers are estimated to have a significant effect. Table 10 presents the results for non-hub routes. Note that there are only about 280 such routes in our sample.

Many of the estimates lose significance in this sub-

sample, although the signs are generally consistent with those estimated earlier. In general, the precipitation variables and the precipitation-freezing interaction have positive coefficients; however, in two specifications, the coefficient no the interaction term is negative (though quite close to zero). The coefficients on the number of flights variables - MAX_CAR_FLTS and MIN_CAR_FLTS - are positive and the first is significant when those variables are included on their own. However, they become insignificant when the passenger variables are also included in columns (5) and (6). The passenger variables themselves are all estimated to have the expected effects on the likelihood of using an owned regional. In contrast to earlier tables, both the direct and connecting passenger variables are estimated to have positive and significant effects on the probability of using

an owned regional, with the coefficient on connecting passengers being larger. This is perhaps because, among non-hub routes, there is a greater amount of variation in the number of connecting passengers (which allows the coefficient to be identified) than there is among hub routes, which account for about 80% of the full sample. ii. Robustness Though not reported, we include several control variables to test the robustness of the results. We re-estimate the specifications from Table 7 including a measure of the distance of the route and the mean of the populations in the endpoint cities. The results are robust to the inclusion of these control variables. iii. Airport Level Results The results so far relate a major’s decision to use an owned regional on a particular city pair to characteristics of that city pair which proxy for the marginal impact of the regional’s effort on the major’s revenues. However, given that majors often serve a large number of city pairs out of a given airport, one might question whether majors actually choose the type of regional to use on a city pair-by-city pair basis or whether majors choose which type of regional to use at a particular airport and then use that same regional for all city pairs out of that airport which it wants to serve with a regional. This would be the case, for example, if there were economies of scope in having the same regional serve multiple city pairs out of the same airport or if there were contracting complementarities. Empirically, in our sample, we observe majors use both types of regionals at 70 different airports, with four of these airports being hubs. This suggests that, in fact, majors may be making their decisions about which type of regional to use at the city pair level. However, as a robustness check on our city pair level results, we aggregate the data to the airline-airport level and run estimate logit models, with the dependent variable now defined as being equal to one if the airline uses an owned regional at the airport. Airports at which a major uses both types of regionals are excluded. We construct the following airport-level versions of our agency variables: ARPT_HUB equals one if the airport is a hub to the major; FRAC_ARR_HUB equals the fraction of the major’s city

pairs from the airport that arrive at one of its hubs; CAR_DEP_TOT_PASS equals the total number of passengers the major enplanes at the airport in a quarter; PRECIP_AVE, PRECIP_SD and FREEZING are the same weather measures used above, defined for the airport, ARR_MEAN_PRECIP_AVE measures the average annual precipitation at the arrival airports of city pairs departing from the airport; and ARR_MEAN_PRECIP_SD and ARR_MEAN_FREEZING are similarly defined as the average of the other two weather variables across the arrival airports of city pairs departing from the airport The results of the airport-level estimation appear in Tables 11 and 12. Table 11 presents the results for all airports while Table 12 presents the results for non-hubs only. The results in columns (1) and (2) of Table 11 indicate the while hub airports are no more likely to be served by owned regionals, airports at which at large fraction of the major’s city pairs arrive at hubs are more likely to be served by owned regionals. The finding that the airport-level hub variable is not significant is not surprising given that hubs are such a small fraction of the total number of airports served (though a large fraction of all city pairs have a hub on an endpoint) and that many spoke airports are served by owned regionals. Column (3) adds the variable which measures the total number of passengers the major enplanes at the airport.

The coefficient on this variable is positive and

significant indicating that airports at which a major enplanes a greater number of passengers are more likely to be served by an owned regional. Columns (4) and (5) add the variables measuring the weather at the airport itself. The results on the precipitation variables are consistent with those obtained earlier; however, the coefficient on the interaction between precipitation and freezing is no longer significant. Finally, column (6) adds the variables which measure the average weather at the endpoints of the city pairs that the major serves from the airport. The precipitation variables are again positive and significant; however, the inclusion of these variables causes the own weather variables to become insignificant, with the estimated coefficients being quite close to zero. This is perhaps because most city pairs flown by regionals are quite short (due to the nature of the planes they fly) and therefore the endpoints may exhibit very similar weather patterns, preventing both sets of weather variables from being separately identified. Table 12 presents the results for non-hub airports. They are virtually identical

to those in Table 11 which is not surprising given that there are only about 20 airlineairports combinations that are hubs.

VI. Conclusion

This paper develops a simple agency model of vertical integration in the regional airline industry and tests the predictions of the model using data on major carriers’ use of owned and independent regionals on city pairs between the top 300 U.S. airports. Our model incorporates several important institutional features of the regional airline industry. Specifically, because major carriers and their regional partners are inherently involved in joint production (the quality of an itinerary that involves travel on a regional depends on the effort of both the regional and the major), majors using route-level revenue sharing agreements cannot provide regionals with optimal incentives for effort on quality dimensions such as on-time performance and in-flight service. The fact that the regional’s effort on a particular city pair may also affect the major’s revenues on other city pairs out of the endpoint airports further exacerbates this incentive problem. We show that, because of these incentives problems, independent regionals will be associated with an agency cost which is increasing in the marginal impact of the regional’s effort on the major’s revenues on the city pair. On the other hand, independent regionals may be associated with lower operating costs than owned regionals because owned regionals may be more successful in exerting pressure on management for higher wages. The model therefore predicts that owned regionals will be used on city pairs on which the agency costs of an independent regional outweigh any potential operating cost advantage. We test this model by constructing several variables which proxy for the marginal impact of the regional’s effort on the city pair on the major’s revenues. Our results are consistent with agency costs affecting vertical integration decisions in this industry. We find that owned regional partners are more likely to be used on city pairs which have the major’s hub at least one endpoint. We also find that they are more likely to be used on city pairs between airports at which the major operates a larger number of flights and on city pairs on which the major carries a larger number of passengers. Finally, we find that city pairs between airports with less favorable weather conditions - which we expect to be

more prone to weather related delays - tend to be served by owned regionals. These effects are robust to changes in the model specification, to the inclusion of airline specific effects in a subsample that includes only airlines that use both types of regionals, and to estimation at the airport - rather than city pair - level. Together, we take these findings as evidence that agency considerations influence majors’ choices between owned and independent regional partners.

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Table 1 Regional Airline Statistics 2000

Carriers Operating

94

Passengers Enplaned (millions)

84.6

Revenue Passenger Miles (billions)

25.27

Average RPM's per Carrier (millions)

268.8

Available Seat Miles (billions)

42.55

Average Load Factor (percent)

59.39

Departures Completed (millions)

4.46

Airports Served (North America) Average Passenger Trip Length (miles)

729

Aircraft Operated Average Seating Capacity (seats/aircraft) Source: Regional Airline Association (www.raa.org)

299 2,271 31.7

Table 2 Majors and Regional Partners in 2000 Regional carriers in bold are fully owned by the major MAJOR American Airlines

Continental Airlines Delta Air Lines

Northwest Airlines Trans World Airlines United Airlines

US Airways

REGIONAL PARTNER American Eagle Airlines Business Express Continental Express Gulfstream International Airlines Atlantic Coast Airlines/ACJet Atlantic Southeast Airlines Comair SkyWest Airlines Trans States Airlines Express Airlines, I Mesaba Aviation Chautauqua Airlines Trans States Airlines Air Wisconsin Atlantic Coast Airlines Great Lakes Aviation Gulfstream International Airlines SkyWest Airlines Mesa Air Group/Air Midwest Allegheny Airlines Mesa Air Group/CCAir Chautauqua Airlines Colgan Airways Commutair Mesa Air Group/Mesa Airlines Piedmont Airlines PSA Airlines

Source: Regional Airline Association (www.raa.org)

Table 3 Hourly Pilot Salaries of Selected Regionals Aircraft Rank Experience 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

CRJ Comair SkyWest CAPT CAPT Delta Connection $101 $93 $98 $91 $95 $88 $92 $86 $90 $83 $87 $81 $84 $78 $82 $76 $79 $74 $77 $71 $75 $69 $72 $67 $70 $65 $68 $63 $66 $61 $64 $60 $62 $58 $60 $56

Source: www.airlinepilotcentral.com

Table 4 Variable Names and Definitions VARIABLE NAME

DEFINITION

SOURCE

Organizational Form Variables =0 if airline operates flight through an independent regional partner, =1 if airline operates flight through a regional partner which it owns

OAG and RAA

HUB

=1 if either endpoint is carrier’s hub

Authors’ construction

TOTAL_PASS

Carrier’s total passengers on the city pair, 10% quarterly sample

DOT

DIRECT_PASS

Carrier’s direct passengers on the city pair, 10% quarterly sample

DOT

OWNED_REGIONAL Agency Variables

CONNECT_PASS MAX_CAR_FLTS MIN_CAR_FLTS RAINIER_FREEZING

Carrier’s connecting passengers on the city pair, 10% quarterly sample = Major’s # of departing domestic flights from endpoint at which it is larger = Major’s # of departing domestic flights from endpoint at which it is smaller = # of months per year average temperature is below 40F in state of the “rainier” endpoint (based on 1971-2000 data)

RAINIER_PRECIP_AVE

= Average annual precipitation in state of “rainier” endpoint, inches (based on 1971-2000)

RAINIER_PRECIP_SD

= Average standard deviation of annual precipitation in state of “rainier” endpoint, inches (based on 1971-2000) = Interaction of freezing and precipitation variables

RAINIER_FREEZ_PRECIP

DOT OAG

National Climatic Data Center National Climatic Data Center National Climatic Data Center National Climatic Data Center

Control Variables DISTANCE

Distance of the route, in miles

DOT

MEANPOP

Arithmetic mean of the populations of the endpoint cities, 2000 Census

Census Bureau

Table 5 Summary Statistics Routes Served by Either Type of Regional VARIABLE NAME

N

MEAN

ST. DEV.

MIN

MAX

978

0.5501

0.4977

0

1

HUB

978

0.7127

0.4527

0

1

TOTAL_PASS

929

526.84

834.29

1

11021.5

DIRECT_PASS

929

250.47

393.21

0

4071

CONNECT_PASS

929

276.37

591.56

0

7430.5

MAX_CAR_FLTS

978

338.86

206.93

5

832

MIN_CAR_FLTS

978

14.81

16.06

1

167

RAINIER_FREEZING

978

2.69

1.99

0

5

RAINIER_PRECIP_AVE

978

29.15

10.84

2.72

48.44

RAINIER_PRECIP_SD

978

18.97

8.42

5.08

44.01

DISTANCE

978

316.97

205.43

23.12

1076.12

MEANPOP

676

2,420,846

1,427,203

244,694

6,814,593

Organizational Form Variables OWNED_REGIONAL Agency Variables

Control Variables

Table 6A Characteristics of Routes (